Analysis on the Relationship between Financing Constraints and Research and Development from the Perspective of the Location of Top Management Network

Taking listed Chinese companies during 2009–2019 as objects, this paper constructs a multivariate discriminant model to measure the degrees of multiple financing constraints and establishes empirical models to analyze the non-linear relationship between the financing constraints and research and development (R&D) investment. Further, the author investigated how the top management network (TMN) location acts on the relationship between financing constraints and R&D investment. The research provides a robust evidence to an inverted U-shaped relationship between the degrees of financing constraints and corporate R&D investment: appropriate financing constraints promote corporate R&D investment; once passing a turning point, excess financing constraints would suppress corporate R&D investment. Besides, it was learned that TMN location positively moderates the financing constraints and R&D investment. In addition, TMN location plays a more obvious regulating role in non-state-owned enterprises (non-SOEs) than in SOEs. The research clarifies the relationship between financing constraints and R&D investment, as well as the moderating role of TMN location. Empirical evidence was provided to help the government reduce credit discrimination and enterprises to widen financing channels and improve innovation capability.

FAKTOR YANG MEMPENGARUHI HASIL BELAJAR SISWA MENGGUNAKAN ANALISIS DISKRIMINAN MI NURUL IMAN KOTA TANGERANG SELATAN

This study aims to determine what factors influence learning achievement. This study aimed to create a discriminant model of learning achievement from the influencing factors. The object of this research is the students of MI Nurul Iman, South Tangerang City. The variables used are six variables. The dependent variable in this study is the average report card, while the independent variables are motivation (X1), Learning Methods (X2), Teacher Competence (X3), Parental Environment (X3), School Infrastructure (X5), Community Environment (X6), which used as many as 56 samples. Based on the results of the output of the discriminant model using the SPSS version 21 program, it shows that the factors that affect student learning outcomes are School Facilities (X5). The discriminant model formed to determine the discriminant function is from the factors that affect learning achievement. Apart from 1, these variables are not used. Then the following function is obtained: D = -3,980 + 0,369X5

A Discriminant Model For Skill Oriented Prediction of Female Cricketers Depending Upon Selected Performance Parameters

Research Purpose. The study aimed to develop a discriminant model for cricketers on the basis of physiological & anthropometric variables. Material and Methods. The study included sixty female seniors BCCI board players representing five different states with mean age 23.4 ± 2.03, mean height 152.1 ± 3.44, and mean weight 52.4 ± 4.21. A multivariate technique was used to predict the cricket performance by classifying the players into batsmen and pace bowlers on the basis of selected physiological & anthropometrical variables. Results. All the assumptions were positively full-filled (Shapiro-Wilk test p > 0.05 and F = 8.121, p = 0.264 for Box’sM test) for applying discriminant analysis to develop the model. A total of eleven performance variables were initially selected for the study and after applying the stepwise statistical technique of discriminant analysis, the model selected certain variables, namely Muscle Mass (1.311), Fat (-0.182) & Shoulder Diameter (0.292) and showed its effectiveness as the Eigenvalue for the fit model was 0.848. Conclusion. A discriminant function F1 = -29.531 + (1.311 × Muscle Mass) + (-0.182 × Fat) + (0.292 × Shoulder Diameter) was developed. The percentage of total variation explained by the model was 71.9%. A total of 93.2% of the observations were correctly classified using the proposed discriminant model.

Quantitative Comparison between Traditional and Intensive Face-to-Face Education through an Organizational Model

Face-to-face education continues to present benefits in terms of student motivation, even though in COVID-19 scenario, online education has been the model of choice. In addition to the traditional face-to-face style, the intensive face-to-face style remains, which allows greater flexibility for the student. The objective of this study was to compare both educational styles and build an organizational model to improve student satisfaction. Two-way general linear model (GLM) with educational styles and satisfaction as fixed factors and discriminant analysis was applied. The selection of the most discriminant variables was made applying the F of Snedecor, Wilks’-Lambda, and the 1-Tolerance. A discriminant model was built. The four variables with the highest discriminant power were problem-solving communication with students’ representatives and shared knowledge and goals with lectures in the intensive style and frequent communication with administrative officers in the traditional style. In addition, it was found that greater face-to-face attendance did not imply greater coordination and that intensive style students show greater satisfaction. The appropriate duration of face-to-face education can contribute to the design of an innovative hybrid system in the future.

Analysis of Mixed Water Source for Urban Underground Water Pipe Leakage by Using Water Geochemistry Model Calculation

China is a country short of water resources, and the leakage of urban water pipe network not only aggravates the current situation of water shortage, but also causes major accidents such as ground collapse, so it is of great significance to study the discrimination of urban underground pipe leakage. In this paper, the conventional ions and hydrogen and oxygen isotopes of water samples are determined by ion chromatograph and inductively coupled plasma mass spectrometer, and the characteristic factors are selected by cluster analysis and principal component analysis, and the mixed water discrimination model based on conventional ions is established According to the difference of hydrogen and oxygen isotope content between buried pipe water and groundwater, a discrimination model based on hydrogen and oxygen isotope is established, and the two models are combined to discriminate the leakage of buried pipe. The results show that, in terms of conventional ion content characteristics, the water in the pipe network is high in K++Na+ and Cl−, while the shallow groundwater near the pipe network is low in K++Na+ and Cl−, and the accuracy of the discriminant model based on conventional ions reaches 87.5%. In the aspect of hydrogen and oxygen isotope content characteristics, the water in the pipe network is closer to the precipitation line than the shallow groundwater, and establishing a discriminant model based on hydrogen and oxygen isotope can determine the leakage of buried pipes. This study provides a scientific basis for judging the leakage of urban underground pipes.

Accurate Identification of Agricultural Inputs Based on Sensor Monitoring Platform and SSDA-HELM-SOFTMAX Model

The unreliability of traceability information on agricultural inputs has become one of the main factors hindering the development of traceability systems. At present, the major detection techniques of agricultural inputs were residue chemical detection at the postproduction stage. In this paper, a new detection method based on sensors and artificial intelligence algorithm was proposed in the detection of the commonly agricultural inputs in Agastache rugosa cultivation. An agricultural input monitoring platform including software system and hardware circuit was designed and built. A model called stacked sparse denoising autoencoder-hierarchical extreme learning machine-softmax (SSDA-HELM-SOFTMAX) was put forward to achieve accurate and real-time prediction of agricultural input varieties. The experiments showed that the combination of sensors and discriminant model could accurately classify different agricultural inputs. The accuracy of SSDA-HELM-SOFTMAX reached 97.08%, which was 4.08%, 1.78%, and 1.58% higher than a traditional BP neural network, DBN-SOFTMAX, and SAE-SOFTMAX models, respectively. Therefore, the method proposed in this paper was proved to be effective, accurate, and feasible and will provide a new online detection way of agricultural inputs.

The Clinical Course of Rheumatoid Arthritis in Kazakhstani Patients

BACKGROUND: Rheumatoid arthritis (RA) prevalence according to the worldwide epidemiological data varies from 0.4% to 1.3%. The disability and mortality rate in RA is high. RA clinic is various, and compiles from articular and systemic manifestations. AIM: The aim of our study was to investigate the clinical course of RA in Kazakhstani patients living in North region of our country. METHODS: The 81 women at the age of 30–55 years with a verified diagnosis of RA who have lived in Kazakhstan for at least 10 years were recruited to the study. All participants were examined by the rheumatologist and a standard laboratory examination was carried out. Statistical analysis was conducted in IBM SPSS Statistics 26 software (IBM.USA;1). RESULTS: The statistically significant higher frequency of erosive radiological stages, bone ankylosis (χ2 = 18.070 df = 6 p = 0.005) was found in seropositive (rheumatoid factor [RF]+) anti-citrullinated protein/peptide antibody positive (ACPA+) subgroup. The correlation analysis showed strong association between certain RA form activity and inflammatory markers, as well as disease triggers. The discriminant model which predicts the stage of radiological damage was obtained. The sensitivity of model in predicting X-ray Stage I-71.6%, Stage II-29.4%, Stage III-37.5%, and Stage IV-63.6%. CONCLUSION: The debut of the RA on average occurred in the third decade of the patients’ life. The joint syndrome had a more unfavorable character RF+ACPA+ patients’ subgroup; however, RF+ACPA-negative (ACPA-) subgroup also showed a predisposition to poorer prognosis. The obtained discriminant model may be useful for RA patients’ management.

Adaptation of farmers to climate-change and determinants of their adaptation decisions in South India

Agriculture, with its allied sectors, is the largest source of livelihood in India and now faces a serious challenge from climate-change. In this context, this study investigated, how farmers in India perceive climate-change, what adaptation strategies they practice, and major determinants of their adaptation decisions. Primary data forms the database collected from 400 sample farmers from southern States of India to employ Discriminant model and Multinomial Logit Model (MNLM). The results of Discriminant analysis revealed that off-farm income, income from agriculture and farming experience are the major discriminating variables with largest contributions to motivate the farmers for tackling climate-change. Findings from MNLM revealed that in addition to above three variables, access to climate-change information and education background of the farmers are also the important determinants in adoption of climate-change adaptation strategies viz., crop diversification, integrating crops with livestock, change in the planting date and adoption of soil and water conservation measures. The study highlighted increasing role of Government in the future to safeguard interests of farmers through offering a wide range of institutional, policy and technology support. Further, providing off-farm employment opportunities to the farmers is crucial to sustain their livelihoods, as such activities are less sensitive to climate-change.